Search Results (32,061)

With the advancement of autonomous and electric vehicles, an increasing demand has been observed for the automatic robot-controlled charging of electric vehicles. The idea of developing such charging stations was raised at several research institutions and universities as early as the 2010s, however the appearance of automatic charging stations with higher Technology Readiness Levels (TRL) can only be dated from 2019 onwards. In most of the developed concepts and solutions, a dedicated parking system is required by vehicle drivers, since the operating range of the robots used for charging is limited. In most cases, solutions do not incorporate robots with unique geometries; instead, proven industrial solutions are applied. The robots in these prototypes are typically installed in a fixed position, similar to industrial applications, and are not mobile. The charging of one vehicle is usually performed by one robot. A high-level summary of the developed mechanical system is presented in this project overview. In this research, an automated, robot-controlled electric vehicle charging system was designed, in which vehicles are parked perpendicularly adjacent to each other, and multiple vehicles are charged using a single collaborative robot. The mechanical system was implemented with a robot mounted on an extendable arm attached to a carriage, which is guided in two directions along rails. In this manner, the automatic charging system is positioned precisely at the parking location of the vehicle to be charged. Full article

As the demand for context-aware services in smart environments continues to rise, Indoor Positioning Systems (IPSs) have evolved from auxiliary technologies into indispensable components of mission-critical infrastructure. This paper presents a comprehensive, multidimensional evaluation of IPSs through the lens of critical infrastructure, addressing both their technical capabilities and operational limitations across dynamic indoor environments. A structured taxonomy of IPS technologies is developed based on sensing modalities, signal processing techniques, and system architectures. Through an in-depth trade-off analysis, the study highlights the inherent tensions between accuracy, energy efficiency, scalability, and deployment cost—revealing that no single technology meets all performance criteria across application domains. A novel evaluation framework is introduced that integrates traditional performance metrics with emerging requirements such as system resilience, interoperability, and ethical considerations. Empirical results from long-term Wi-Fi fingerprinting experiments demonstrate the impact of temporal signal fluctuations, heterogeneity features, and environmental dynamics on localization accuracy. The proposed adaptive algorithm consistently outperforms baseline models in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), confirming its robustness under evolving conditions. Furthermore, the paper explores the role of collaborative and infrastructure-free positioning systems as a pathway to achieving scalable and resilient localization in healthcare, logistics, and emergency services. Key challenges including privacy, standardization, and real-world adaptability are identified, and future research directions are proposed to guide the development of context-aware, interoperable, and secure IPS architectures. By reframing IPSs as foundational infrastructure, this work provides a critical roadmap for designing next-generation indoor localization systems that are technically robust, operationally viable, and ethically grounded. Full article

Accurate characterization of surface solar irradiance at fine spatial, temporal, and spectral resolution is central to applications such as solar energy and environmental monitoring. On the one hand, modeling radiative transfer to achieve such accuracy requires detailed characterization of a wide range of factors, including the vertical profiles of gaseous and particulate absorbers and scatterers, wavelength-resolved surface reflectivity, and the three-dimensional morphology of clouds. On the other hand, satellite-based remote sensing products typically provide top-of-the-atmosphere irradiance at coarse spatial resolutions, where individual pixels can span several kilometers, failing to capture fine-scale intra-pixel variability. In this study, we introduce a machine learning framework that integrates large-scale remote sensing satellite data with hyperlocal, second-by-second ground-based measurements from an ensemble of low-cost spectral sensors to estimate the wavelength-resolved surface solar irradiance spectra at the hyperlocal level. The satellite data are obtained from the Harmonized Sentinel-2 MSI (MultiSpectral Instrument), Level-2A Surface Reflectance (SR) product, which offers high-resolution surface reflectance data. By leveraging machine learning, we model the relationship between satellite-derived surface reflectance and ground-based spectral measurements to predict high-resolution, wavelength-resolved irradiance, using target data obtained from an NIST-calibrated reference instrument. By utilizing a low-cost sensor ensemble that is easily deployable at scale, combined with downscaled satellite data, this approach enables accurate modeling of intra-pixel variability in surface-level solar irradiance with high temporal resolution. It also enhances the utility of the Harmonized Sentinel-2 MSI data for operational remote sensing. Our results demonstrate that the model is able to estimate surface solar irradiance with an R² ≈ 0.99 across all 421 spectral bins from 360 nm to 780 nm at 1 nm resolution, offering strong potential for applications in solar energy forecasting, urban climate research, and environmental monitoring. Full article

Unmanned maritime vehicles (UMVs) have become essential tools in marine research and monitoring, significantly enhancing operational efficiency and reducing risks and costs. Fiber-reinforced composites have been widely used in marine applications due to their excellent characteristics. However, environmental concerns and the pursuit of sustainable development goals have driven the development of environmentally friendly materials. The development of eco-friendly biocomposites for UMV construction can effectively reduce the environmental impact of marine equipment. This study investigates the effects of seawater aging on kenaf/flax/glass-fiber-reinforced composites under artificial seawater conditions and determines their ranking for UMVs using the Analytic Hierarchy Process (AHP). These hybrid composites, fabricated with various stacking sequences, were prepared using a combination of hand lay-up and vacuum bagging techniques. All plant fibers underwent sodium hydroxide treatment to eliminate impurities and enhance interfacial bonding, while nano-silica was incorporated into the epoxy matrix to improve overall performance. After 50 days of immersion in artificial seawater, mechanical tests were conducted to evaluate the extent of changes in mechanical properties. Subsequently, the AHP analysis was performed based on three main criteria and thirteen sub-criteria to determine the most suitable configuration for marine applications. The results demonstrate that the stacking sequence plays a critical role in resisting seawater-induced degradation and maintaining mechanical performance. GKFKG exhibited the highest retention rates for both tensile strength (86.77%) and flexural strength (88.36%). Furthermore, the global priority vector derived from the AHP analysis indicates that hybrid composites consisting of kenaf, flax, and glass fibers consistently ranked highest. The optimum configuration among these hybrid composites was determined to be GKFKG, followed by GFKFG, GKKKG, and GKGKG. Full article

Hippophae rhamnoides subsp. sinensis Rousi has high ecological and medicinal value, and it is an important plant resource unique to the arid regions of Northwest China. Exploring the influence of climate characteristics and soil factors on the composition, diversity, and function of the rhizosphere bacterial community of Chinese seabuckthorn is of great value for developing and popularizing characteristic plant resources in the arid regions of Northwest China. In this study, the rhizosphere soil of 13 Chinese seabuckthorn distribution areas in the northwest of China was taken as the research object, the bacterial community map was constructed based on 16S rRNA gene high-throughput sequencing technology, and the species abundance composition, structural diversity, molecular co-occurrence network, and phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt), as well as the function of rhizosphere soil bacterial community, were systematically studied. Combined with Mantel test and redundancy analysis (RDA), the key habitat factors driving the rhizosphere soil bacterial community structure of Chinese seabuckthorn were explored. The results showed that: (1) The number of amplicon sequence variants (ASVs) in rhizosphere soil bacterial community of Chinese seabuckthorn was the highest in S2(3072) and the S12(3637), and the lowest in the S11(1358) and S13(1996). The rhizosphere soil bacterial community was primarily composed of Proteobacteria, Actinobacteriota, and Acidobacteriota. Except for the S6 and S11 habitats, the dominant bacterial genera were mainly Achromobacter, Acidobacter (RB41), and Sphingomonas. (2) The α and β diversity of rhizosphere soil bacterial communities of Chinese seabuckthorn across 13 distribution areas were significantly different. The number of operational taxonomic units (OTUs), Ace index, and Chao 1 index of soil bacterial community in the S12 distribution area are the highest, and they are the lowest in S11 distribution area, with significant differences. The aggregation of soil bacterial communities in the S5 and S10 distribution areas is the highest, while it is the lowest in the S6 and S11 distribution areas. (3) PICRUSt function classification of soil bacteria showed that Metabolism and Genetic Information Processing functions were the strongest across all distribution areas, with S10 exhibiting higher functional capacity than other areas and S11 showing the weakest. (4) Cluster analysis revealed that soil bacteria across the 13 distribution areas were clustered into two groups, with S10 and S12 distribution areas as one group (Group 1) and the remaining 11 distribution areas as another group (Group 2). (5) Redundancy analysis revealed that pH was the key soil environmental factor driving the rhizosphere soil bacterial community α-diversity of Chinese seabuckthorn, followed by altitude (ALT) and soil water content (SWC). In summary, Chinese seabuckthorn prefers neutral to alkaline soils, and environmental factors play an important role in driving bacterial diversity, community structure, functional profiles, and co-occurrence networks in rhizosphere soil of Chinese seabuckthorn. Full article

Reducing carbon dioxide emissions in freight transportation is considered a key objective in contemporary sustainable supply chain management. While several tools and standards have been developed to estimate transport-related emissions, most rely on static assumptions, generic emission factors and are limited to single-scenario evaluation. Therefore, their operational applicability remains restricted especially in dynamic and complex environments where fast responsiveness is essential. Moreover, these tools are often disconnected from real-world constraints and rarely incorporate expert’s input. To address this gap, this study introduces a hybrid decision-support CO₂ assessment framework combining theoretical models with field-based inputs. The proposed approach combines structured interviews conducted with 300 supply chain consultants and is operationalized through a dynamic digital tool that enables users to simulate multiple scenarios simultaneously. The tool accounts for critical variables including transport mode, routing distance, vehicle configuration, and shipment characteristics, thereby enabling a contextualized and flexible analysis of carbon emissions. A validation case study was conducted to confirm the applicability of the tool to industrial settings. Computational results show significant variation in emissions across different routing strategies and modal configurations, highlighting the tool’s capacity to support environmentally informed decisions. This research offers both a replicable methodology and a practical contribution: a user-centered, multi-scenario tool that improves the accuracy, adaptability, and strategic value of CO₂ emission calculations in freight transport planning. Full article

Although numerous reviews have discussed the research progress in “filtration-type” oil–water membrane separation with special wettability, they predominantly focus on the types of membrane separation and preparation methods, without providing an in-depth analysis of the separation principles and strategies. This paper is different from the previous reviews focusing on the types and preparation methods of membrane separation, mainly as regards membrane surface adsorption, liquid through the pores, and liquid extraction from the pores of the three key nodes in order to analyze the impact of membrane block wettability on the oil–water separation effect of the independent influence. Accordingly, we summed up the membrane separation principle and design strategy to guide modular wettability design during membrane fabrication, thereby enhancing membrane wettability. The modular wettability design approach can provide guidance during the membrane development phase, offering potential solutions to extend membrane lifespan and address issues of surface fouling and pore clogging while enhancing mass transfer efficiency during operation. Full article

This study addresses the selection and application of composite materials for aerospace systems operating in extreme environmental conditions, with a particular focus on high-altitude pseudo-satellites (HAPS). This research is centered on the development of a 400 kg autonomous aerial vehicle (AAV) capable of sustained operations at altitudes of up to 30 km. KMU-3’s microstructure, comprising high-modulus carbon fibers (5–7 µm diameter) in a 5-211B epoxy matrix, provides a high specific strength (1000–2500 MPa), low density (1.6–1.8 g/cm³), and thermal stability (−60 °C to +600 °C), ensuring structural integrity in stratospheric conditions. The mechanical, thermal, and aerodynamic properties of KMU-3-based truss structures were evaluated using finite element method (FEM) simulations, computational fluid dynamics (CFD) analysis, and experimental prototyping. The results indicate that ultra-thin KMU-3 with a wall thickness of 0.1 mm maintains structural integrity under dynamic loads while minimizing overall mass. A novel thermal bonding technique employing 5-211B epoxy resin was developed, resulting in joints with a shear strength of 40 MPa and fatigue life exceeding 10⁶ cycles at 50% load. The material properties remained stable across the operational temperature range of −60 °C to +80 °C. An optimized fiber orientation (0°/90° for longerons and ±45° for diagonals) enhanced the resistance to axial, shear, and torsional stresses, while the epoxy matrix ensures radiation resistance. Finite element method (FEM) and computational fluid dynamics (CFD) analyses, validated by prototyping, confirm the performance of ultra-thin (0.1 mm) truss structures, achieving a lightweight (45 kg) design. These findings provide a validated, lightweight framework for next-generation HAPS, supporting extended mission durations under harsh stratospheric conditions. Full article

Electric vehicles (EVs) are the fastest-growing type of transport. Battery packs are a key component in EVs. Modern lithium-ion battery cells are characterized by low self-discharge current, high power density, and durability. At the same time, the battery management system (BMS) plays a pivotal role in ensuring high efficiency and durability of battery cells and packs. The BMS monitors and controls the battery charge and discharge to ensure EV safety and optimum operation. This paper is devoted to analyzing BMS circuitry configurations and algorithms. The analysis includes circuit solutions and algorithms for implementing the main BMS functions, such as parameter monitoring, protection, cell balancing, state estimation, charging and discharging management, communication, and data logging. The paper provides insights into the recent research literature on BMS, and the advantages and disadvantages of methods for implementing BMS functions are compared. The paper also discusses the application of artificial intelligence technologies and aspects of further work on next-generation BMS technologies. Full article

Reliable operation of marine equipment is crucial for ensuring vessel performance and safeguarding the safety of personnel and the marine environment. However, the complexity of evaluation criteria and the subjectivity inherent in expert judgments pose significant challenges for effective reliability assessment. To address these challenges, this study proposes an extended VIKOR method within a group decision-making (GDM) framework based on picture fuzzy numbers. The method first collects expert evaluations through questionnaires and voting to construct individual decision matrices, and then it applies a newly developed entropy-based approach to determine attribute weights, resulting in a group-weighted decision matrix. Subsequently, an extended VIKOR model is introduced, where the group utility measure is derived from one positive reference matrix and two negative reference matrices, while the group regret measure is based on two negative reference matrices. To improve assessment precision, this study also introduces a novel normalized projection measure to evaluate the closeness between decision matrices. Finally, two ranking strategies are developed, for static and dynamic environments, respectively. The proposed method is validated through a case study on marine equipment reliability assessment, confirming its effectiveness and feasibility. This study provides valuable insights for both theoretical research and practical applications in maritime engineering. Full article

Background: The increasing integration of automated transport drones into logistics networks presents transformative potential for addressing contemporary logistics challenges, particularly in last-mile delivery, healthcare, disaster response, urban mobility, and postal services. However, their effective integration into varied logistics contexts remains hindered by infrastructure, regulatory, and operational limitations. This study aims to explore how drone-based logistics systems can be systematically planned and evaluated across diverse operational environments. Methods: A structured literature review was conducted, employing thematic synthesis to analyze current research on drone logistics. The analysis focused on identifying the key planning dimensions and interrelated components that influence the deployment of drone-based transport systems. Results: The review identified seven central planning dimensions: areas of application, system components, transport configuration, geographic areas, optimization and analysis methods, logistical planning, and performance assessment. These dimensions inform a conceptual framework designed to guide the planning and assessment of drone logistics networks. Conclusions: While existing studies contribute valuable insights into route optimization and drone deployment strategies, they often overlook integrative approaches that account for societal and environmental factors. The study emphasizes the need for interdisciplinary collaboration and context-specific planning frameworks to enhance the sustainable and effective implementation of drone-based logistics systems. Full article

Drone power management poses ongoing challenges that significantly impact operational effectiveness across various applications. This research examines path planning optimization, particularly focusing on distance minimization to enhance efficiency and performance. When drones must visit static ground stations, analyzing the constituent elements of flight paths reveals that segments connecting the launch pad to initial and final stations emerge as a distinct area for further path optimization. Given scenarios where launch pad relocation remains feasible, this study proposes several alternative methodologies for adjusting launch positions to minimize total flight distances across multiple drone operations. The investigation employed extensive experimentation involving diverse configurations with varying station counts and available drone units. Results demonstrate that repositioning the launch pad to serve as an optimal center point for all drone routes yields substantial improvements in total distance minimization, ranging from 4% to 22% across different operational scenarios. The geometric median approach consistently outperformed alternative positioning strategies, achieving these improvements while maintaining computational efficiency. These findings contribute to sustainable drone operations by reducing energy consumption through optimized flight planning. The methodology proves particularly valuable for applications requiring flexible launch point positioning, offering practical solutions for enhancing operational efficiency in environmental monitoring, precision agriculture, and infrastructure inspection tasks where energy conservation directly impacts mission success and operational viability. Full article

This research presents a novel methodology for lightweighting and cost reduction of components with high structural demands by integrating advanced design and manufacturing techniques. Specifically, it combines topology optimization (TO) with additive manufacturing (AM), also known as 3D printing. Unlike conventional approaches, the proposed method first determines the optimal geometry using an artificially stiff material, and only then evaluates real materials for structural and manufacturing feasibility. This design-first, material-second strategy enables broader material screening and maximizes weight reduction without compromising performance. The proposed workflow is applied to the design of a turbofan air intake—an aeronautical component operating under supersonic conditions—addressing both structural integrity and manufacturing feasibility. Three materials from distinct classes are assessed: two metallic alloys (aluminum alloy 6061 and titanium alloy, Ti6Al4V) and a high-performance polymer (polyetheretherketone, PEEK). This last option is preliminarily discarded after being analyzed for this specific application. Finite element (FE) simulations are used to evaluate the mechanical behavior of the optimized geometries, including bird-strike conditions. Among the evaluated manufacturing techniques, Selective Laser Melting (SLM) is identified as the most suitable for the metallic materials selected, providing an effective balance between performance, manufacturability, and aerospace compliance. This study illustrates the potential of TO–AM synergy as a sustainable and efficient design approach for next-generation aerospace components. Simulation results demonstrate a weight reduction of up to 71% while preserving critical functional regions and maintaining structural integrity in Al 6061 and Ti6Al4V cases, under the diverse loading conditions typical of real flight scenarios, while PEEK remains an attractive option for uses where mechanical demands are less stringent. Full article

This paper investigates how security-by-design principles can be integrated into urban planning to achieve a balance between protective measures and the openness, accessibility, and aesthetic quality of public spaces. Addressing a current gap in urban design practice, we introduce a new evaluative framework—the SAFE-CITIES “Atlas 4 Safe Public Spaces”—that embeds European policy guidelines, CPTED concepts, and New European Bauhaus values into an integrated security-by-design assessing tool. Drawing on the Horizon Europe SAFE-CITIES project (Grant Agreement No. 101073945), the research combines theoretical insights from EU policy documents and design principles with a comparative analysis of two case studies (Barcelona and Copenhagen) to inform practical strategies for integrating safety considerations into the design process. This approach identifies key operational principles that illustrate how safety measures—if considered from the early-stage planning—can be integrated without compromising openness and livability of public, illustrating how early-stage planning can incorporate security measures while sustaining social interaction and community life. Overall, the findings show that safety can be built into public space design from the outset, reinforcing community engagement and resilience, and the proposed Atlas framework offers planners a concrete tool to align security objectives with on-the-ground urban design practice. Full article

Franchising operates as a proven business model that drives substantial growth for small and medium-sized enterprises (SMEs) worldwide. The franchise ecosystem in Saudi Arabia lacks sufficient research, despite established frameworks for success in markets such as the United States, the United Kingdom, and Australia. This research investigates the elements that lead to franchise success in Saudi Arabia through a combination of qualitative and quantitative data. This research evaluates franchise performance through metrics such as Average Revenue Per Unit (ARPU), Return on Investment (ROI), Franchise Success Rate, Time to Break Even, and Market Growth Rate, comparing Saudi Arabia with the U.S., the U.K., and India to identify essential success determinants. The research reveals that franchise success depends on regulatory frameworks, cultural alignment, economic diversification, and supply chain efficiency. The U.S. and U.K. enjoy established legal protections, whereas Saudi Arabia faces regulatory complexities and resource limitations. The research proposes three strategic recommendations: government incentives, locally adapted business models, and carefully selected locations to boost franchise success. The analysis provides essential information to policymakers, franchisors, and entrepreneurs seeking to expand their businesses in Saudi Arabia. The implementation of Vision 2030 growth barrier solutions and market opportunities will enable Saudi Arabia to build up its franchising sector and enhance market performance. This research adds new knowledge to the franchising literature in emerging markets and its impact on sustainable business growth. Full article